Enterprises employ large, complex, computing environments that include a number of enterprise components such as servers, routers, databases, mainframes, personal computers, intelligent agents and business applications, for example. Systems that monitor complex enterprise computing environments are known in the art. From time to time, such monitoring systems monitor and analyze the performance of enterprise components, and it is useful for such monitoring systems to have rapid access to certain metrics regarding performance of the components being analyzed. Such metrics may be sampled on-demand in real-time or fetched from a large historical data repository.
Typically, large repositories of historical data describing enterprise component performance are created over time by enterprise monitoring systems configured to track and record performance data for certain enterprise components or groups of components. Such performance data may be useful in analyzing the operation of a component or group of components, for example, to schedule future operations or to report the performance of the component(s) over time.
Enterprises typically have certain criteria to determine whether and which components are subject to performance monitoring. Over time, changes to the configuration of an enterprise system, changes to the criteria for collecting performance data, and the addition and removal of enterprise components may result in an incomplete performance history for any particular component. Consequently, such historical data repositories are complex stores which may not include data for every enterprise component, or for every time period.
Gaps in historical performance data may adversely affect the ability of the monitoring system to project the future performance of a particular component. Consequently, there is a need for methods and systems that rapidly provide an estimate of historical performance of an enterprise component despite incomplete historical performance data.